df <- read.csv("merged-new-version2.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]
df
df$ln_novelty <- log(df$novelty+1)
df$ln_total <- log(df$total+1)
df$ln_exploration <- log(df$exploration+1)
df$group = factor(df$group)
df
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_exploration ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_exploration ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-0.2275 -0.1862 -0.1563 0.1866 0.5328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.22749 0.01941 11.719 <2e-16 ***
factor(group)0 -0.06718 0.02710 -2.479 0.0134 *
factor(group)1 -0.04128 0.02678 -1.542 0.1236
factor(group)2 -0.03052 0.02662 -1.146 0.2521
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2362 on 632 degrees of freedom
Multiple R-squared: 0.009905, Adjusted R-squared: 0.005206
F-statistic: 2.108 on 3 and 632 DF, p-value: 0.09805
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_total ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-4.7373 -0.2143 0.3493 0.8471 1.7667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.1441 0.1181 43.541 < 2e-16 ***
factor(group)0 -1.0417 0.1649 -6.316 5.05e-10 ***
factor(group)1 -0.4069 0.1630 -2.497 0.012787 *
factor(group)2 -0.5990 0.1620 -3.697 0.000237 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.437 on 632 degrees of freedom
Multiple R-squared: 0.06155, Adjusted R-squared: 0.0571
F-statistic: 13.82 on 3 and 632 DF, p-value: 9.76e-09
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group), data=df)
summary(mod)
Call:
lm(formula = ln_novelty ~ factor(group), data = df)
Residuals:
Min 1Q Median 3Q Max
-0.52892 -0.14068 0.06865 0.15783 0.28954
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.52892 0.01773 29.837 < 2e-16 ***
factor(group)0 -0.13269 0.02475 -5.362 1.16e-07 ***
factor(group)1 -0.12367 0.02445 -5.058 5.56e-07 ***
factor(group)2 -0.05178 0.02431 -2.130 0.0336 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2157 on 632 degrees of freedom
Multiple R-squared: 0.05844, Adjusted R-squared: 0.05397
F-statistic: 13.08 on 3 and 632 DF, p-value: 2.706e-08
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod)
Call:
lm(formula = ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.6625 -0.1580 -0.1158 0.1618 0.5694
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.233602 0.038414 6.081 2.10e-09 ***
factor(group)0 -0.055198 0.026422 -2.089 0.0371 *
factor(group)1 -0.036783 0.026092 -1.410 0.1591
factor(group)2 -0.022188 0.025726 -0.862 0.3888
Q7_Q7_1 -0.003198 0.007597 -0.421 0.6740
Q7_Q7_2 0.005396 0.007728 0.698 0.4853
Q8_Q8_1 -0.013705 0.007994 -1.714 0.0870 .
Q10 -0.003711 0.011739 -0.316 0.7520
count 0.025482 0.003090 8.248 9.92e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2257 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1106, Adjusted R-squared: 0.09895
F-statistic: 9.497 on 8 and 611 DF, p-value: 1.997e-12
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod)
Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.73108 -0.10789 0.05269 0.14730 0.30517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.412100 0.035171 11.717 < 2e-16 ***
factor(group)0 -0.113961 0.024192 -4.711 3.06e-06 ***
factor(group)1 -0.116408 0.023889 -4.873 1.40e-06 ***
factor(group)2 -0.051286 0.023555 -2.177 0.02984 *
Q7_Q7_1 -0.020611 0.006956 -2.963 0.00316 **
Q7_Q7_2 0.028904 0.007075 4.085 4.99e-05 ***
Q8_Q8_1 0.008860 0.007319 1.210 0.22656
Q10 0.007122 0.010748 0.663 0.50783
count 0.013293 0.002829 4.699 3.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2067 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1234, Adjusted R-squared: 0.112
F-statistic: 10.75 on 8 and 611 DF, p-value: 3.249e-14
df$group <- relevel(df$group, ref = "3")
mod1 <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod1)
Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 +
Q8_Q8_1 + Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-0.73108 -0.10789 0.05269 0.14730 0.30517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.412100 0.035171 11.717 < 2e-16 ***
factor(group)0 -0.113961 0.024192 -4.711 3.06e-06 ***
factor(group)1 -0.116408 0.023889 -4.873 1.40e-06 ***
factor(group)2 -0.051286 0.023555 -2.177 0.02984 *
Q7_Q7_1 -0.020611 0.006956 -2.963 0.00316 **
Q7_Q7_2 0.028904 0.007075 4.085 4.99e-05 ***
Q8_Q8_1 0.008860 0.007319 1.210 0.22656
Q10 0.007122 0.010748 0.663 0.50783
count 0.013293 0.002829 4.699 3.23e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2067 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1234, Adjusted R-squared: 0.112
F-statistic: 10.75 on 8 and 611 DF, p-value: 3.249e-14
anova(mod, mod1)
Analysis of Variance Table
Model 1: ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count
Model 2: ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 +
count
Res.Df RSS Df Sum of Sq F Pr(>F)
1 611 26.099
2 611 26.099 0 0
library(lmerTest)
fit.lmer <- lmer(ln_novelty ~ factor(group) + ( 1 | phase), data = df, REML= FALSE)
fit.lmer
Linear mixed model fit by maximum likelihood ['lmerModLmerTest']
Formula: ln_novelty ~ factor(group) + (1 | phase)
Data: df
AIC BIC logLik deviance df.resid
-138.4479 -111.7167 75.2239 -150.4479 630
Random effects:
Groups Name Std.Dev.
phase (Intercept) 0.005242
Residual 0.214918
Number of obs: 636, groups: phase, 4
Fixed Effects:
(Intercept) factor(group)0 factor(group)1 factor(group)2
0.52892 -0.13269 -0.12367 -0.05178
tapply(df$ln_novelty, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.4842 0.5588 0.5289 0.6162 0.6894
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.5206 0.3962 0.6073 0.6858
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.1777 0.5062 0.4053 0.6182 0.6931
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.3871 0.5465 0.4771 0.6084 0.6904
tapply(df$ln_total, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
4.331 4.761 5.079 5.144 5.515 5.891
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 3.991 4.830 4.102 5.337 5.869
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.553 5.089 4.737 5.580 5.882
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000 4.615 4.925 4.545 5.450 5.884
tapply(df$ln_exploration, df$group, summary)
$`3`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0938 0.2275 0.4391 0.6931
$`0`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.0000 0.0000 0.0000 0.1603 0.3010 0.6931
$`1`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.00000 0.02175 0.18621 0.38244 0.69315
$`2`
Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00000 0.00000 0.09391 0.19697 0.35899 0.69315
library(vtree)
Registered S3 methods overwritten by 'htmltools':
method from
print.html tools:rstudio
print.shiny.tag tools:rstudio
print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
method from
print.htmlwidget tools:rstudio
vtree version 5.6.5 -- For more information, type: vignette("vtree")
vtree(df, "group")
vtree(df, c("phase", "group"),
fillcolor = c( phase = "#e7d4e8", group = "#99d8c9"),
horiz = FALSE)
df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod)
Call:
lm(formula = ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 +
Q10 + count, data = df)
Residuals:
Min 1Q Median 3Q Max
-4.6309 -0.2310 0.3346 0.7764 1.9667
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.82832 0.22926 21.060 < 2e-16 ***
factor(group)0 -0.98353 0.15769 -6.237 8.33e-10 ***
factor(group)1 -0.42360 0.15572 -2.720 0.006709 **
factor(group)2 -0.59841 0.15354 -3.897 0.000108 ***
Q7_Q7_1 -0.19585 0.04534 -4.319 1.83e-05 ***
Q7_Q7_2 0.19627 0.04612 4.256 2.41e-05 ***
Q8_Q8_1 -0.10504 0.04771 -2.202 0.028060 *
Q10 0.17920 0.07006 2.558 0.010776 *
count 0.12749 0.01844 6.914 1.19e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.347 on 611 degrees of freedom
(16 observations deleted due to missingness)
Multiple R-squared: 0.1768, Adjusted R-squared: 0.166
F-statistic: 16.4 on 8 and 611 DF, p-value: < 2.2e-16
with(df, interaction.plot(group, phase, ln_total, ylim=c(0, max(ln_total)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot

with(df, interaction.plot(group, phase, ln_exploration, ylim=c(0, max(ln_exploration)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot

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